How to Split Work for Parallel AI Agents

Running five AI agents at once sounds like a five-fold speedup. In practice, the speedup is entirely decided before the agents start — by how you split the work. Hand five agents five tasks that all touch the same files, and you’ve just created five branches that fight each other at merge time. Hand them five genuinely independent tasks, and you get the real speedup.

The hard skill in parallel AI development isn’t running the agents. It’s carving the work so they don’t collide. This guide is about that carving.

If you haven’t set up the underlying workflow yet, start with running multiple AI coding agents on one repo and the git worktree isolation that makes it possible. This article assumes you can run agents in parallel — and focuses on what to give them.

Why Decomposition Is the Real Bottleneck

Isolation solves the mechanical problem: two agents can’t overwrite each other’s files because each works in its own worktree. But isolation doesn’t make the changes independent. Two agents can edit different copies of the same file in parallel and still produce two diffs that can’t both land cleanly.

So the goal of decomposition is independence at merge time, not just at edit time. A good split has three properties:

Disjoint file sets. Tasks touch different files, or at least different regions of different files.
No shared contract churn. Tasks don’t each redefine the same function signature, type, or API shape.
Mergeable in any order. You can merge the branches in whatever sequence they finish, with few or no conflicts.

Get those three right and parallel execution is close to free. Get them wrong and you pay it all back during integration.

The Core Move: Split Along Seams, Not Steps

The instinct is to split a feature into steps: design the schema, then write the API, then build the UI, then add tests. But steps are sequential by definition — the API needs the schema, the UI needs the API. Steps don’t parallelize.

What parallelizes is seams: natural boundaries where one part of the codebase talks to another through a stable interface. The common ones:

By layer — frontend vs backend vs database migration
By feature surface — the settings page vs the export feature vs the notification system
By file or module — auth/, billing/, search/ each owned by one agent
By concern — implementation in one task, tests in another, docs in a third

The test for a good seam: if I describe each side without mentioning the other, do both descriptions still make sense? If yes, they can run in parallel. If you can’t describe task A without referencing task B’s internals, they’re really one task.

Map the Dependency Graph First

Before you create any tasks, spend two minutes sketching what depends on what. You’re looking for the shape of the graph:

        ┌─ API endpoints ─┐
schema ─┤                 ├─ integration tests
        └─ UI components ─┘

Here, the schema is a dependency for both the API and the UI, and the integration tests depend on everything. Only the middle layer (API + UI) is genuinely parallel — and only after the schema exists. That shared piece at the root of the graph points to the most useful pattern in parallel work.

Pattern: Contract First, Then Fan Out

Most features have one piece that everything else depends on — a type definition, a database schema, an API contract, an interface. That piece is shared, so it can’t be parallelized away. Do it first, alone, then let the dependent work fan out.

Sequential step: one agent (or you) defines the contract — the TypeScript types, the OpenAPI spec, the table schema. Commit it to main.
Parallel fan-out: now branch off that commit. The backend agent implements the endpoints against the contract. The frontend agent builds components against the same types. The test agent writes fixtures from the schema. None of them touch the contract — they consume it.

Because every branch starts from a main that already contains the agreed-upon types, the parallel tasks rarely conflict. They’re filling in implementations behind a frozen interface.

The mistake to avoid: letting two parallel agents each define the contract. Now you have two incompatible type definitions and a merge conflict in the file everything imports. Freeze the contract before you fan out.

A Worked Example

Say you’re adding a “saved searches” feature: users can name a set of filters and recall them later. A naive single prompt would be “add saved searches.” Here’s how to split it instead.

Step 0 — Contract (sequential, ~5 min). Define the SavedSearch type and the database migration. This is small, shared, and blocks everything. Do it first and merge it.

Then four parallel tasks, each on its own branch:

Task	Touches	Agent fit
API endpoints (CRUD for saved searches)	`api/saved-searches/*`	Codex CLI — clear spec
UI: save/recall dropdown	`components/SavedSearches/*`	Claude Code — matches existing patterns
Migration runner + seed data	`db/migrations/*`	Gemini CLI — mechanical
Integration tests	`tests/saved-searches/*`	Codex CLI — repetitive

Each task names a distinct directory. No two agents edit the same file. The API and UI both import the shared SavedSearch type but neither modifies it. When the branches come back, they merge in any order.

That’s the difference between “add saved searches” run as one sequential agent (~40 minutes) and a contract-first fan-out (~10 minutes of wall-clock time once the contract lands). For matching agents to tasks, see Claude Code vs Codex vs Gemini.

What Parallelizes Well — and What Doesn’t

Parallelizes well:

New files and new modules (no existing code to collide with)
Work behind a stable, already-merged interface
Cross-cutting but mechanical changes scoped to separate directories
Tests, docs, and boilerplate — they consume code without changing it

Parallelizes badly:

Two tasks that both refactor the same function
Renames or signature changes that ripple across the codebase (do these alone, then fan out)
Work where task B’s correct implementation depends on choices task A hasn’t made yet
Anything touching a single hot file everything imports (index.ts barrels, central routers, global config)

When two tasks must touch the same file, that’s your signal to either merge them into one task or sequence them. Forcing them into parallel branches just relocates the work from “writing code” to “resolving conflicts” — and conflict resolution is the part AI agents help with least.

Write Prompts That Respect the Boundaries

A clean split can still collapse if the prompts are sloppy. Two habits keep parallel agents in their lanes:

Name the boundary explicitly. Tell each agent which files it owns and which it must not touch: “Implement the endpoints in api/saved-searches/. Import types from types/search.ts — do not modify that file.” An agent told to stay in its directory usually will.

Point every agent at the same contract. When the shared types already live on main, each prompt can reference them by path. Every branch builds against one source of truth, so the pieces fit when they come back together.

And if a prompt can’t avoid naming another in-flight task’s internals, that’s the same signal from earlier — the two tasks aren’t really parallel.

Common Mistakes

Splitting by step instead of by seam. “Design, then build, then test” is a pipeline, not a parallel plan. Find the seams instead.

Too many tasks, too soon. Five overlapping tasks are slower than two clean ones. The bottleneck shifts to your review and merge capacity. Start with two or three independent tasks and scale once the workflow feels smooth.

Skipping the contract. Letting parallel agents each invent the shared interface guarantees a conflict in the most-imported file. Freeze it first.

Ignoring the merge order. Even well-split tasks can have a soft ordering — merge the foundational and isolated changes first, the riskier integrative ones last. More on this in the review workflow for AI-generated code.

How Parallel Code Automates the Split

Once you’ve decided on the split, Parallel Code handles the mechanics. You create one task per slice; each gets its own branch and worktree automatically, so each task’s edits stay isolated by construction — no agent can overwrite another’s working files. You assign the agent that fits each task, watch them all from one dashboard, and review each branch as a small, focused diff against main instead of one tangled mega-change.

The contract-first pattern maps cleanly onto it: merge the shared-types task first, then create the dependent tasks — they branch off the updated main and fan out from there.

Install from the latest release
Land your shared contract on main
Create one task per independent slice and assign agents
Review and merge each branch as it finishes

Key Takeaways

The speedup from parallel agents is decided by the split, not the agents
Split along seams (stable interfaces) — not steps (sequential phases)
Map the dependency graph first; the shared piece can’t be parallelized
Contract first, then fan out: freeze the interface, then run dependent work in parallel
Aim for disjoint file sets so branches merge in any order
If two tasks must touch the same file, merge them or sequence them — don’t force parallelism